A joining equipment in which a neural network is employed for controlling a joining process. A dynamic analog model is used for neuron elements configuring the network. The equipment includes a detector, a controller, and a neural network. The detector detects a joining state of a joining portion when work pieces are joined with each other. The controller controls the output of the joining equipment. In response to output signals from the detector, the neural network transmits signals to the controller. Such a structure allows the joining equipment to flexibly respond to complicated changes in joining states. Besides, using similarity with a thermal conduction equation enables to minimize the number of input items fed into the neural network. Furthermore, using an approximate solution to the thermal conduction equation realizes to accelerate the time for numerical calculation without loss of accuracy.